Short-chain fatty acid butyrate induces IL-10-producing B cells by regulating circadian-clock-related genes to ameliorate Sjögren's syndrome.

OBJECTIVES: Sjögren's syndrome (SS) is an autoimmune disease caused by inflammation of the exocrine gland. The pathological hallmark of SS is the infiltration of lymphocytes into the salivary glands. Increased infiltration of T and B cells into salivary glands exacerbates symptoms of SS. Several recent studies have identified the role of gut microbiota in SS. Butyrate, one of the metabolites of the gut microbiota, regulates T cells; however, its effects on B cells and SS remain unknown. This study determined the therapeutic effect of butyrate on regulating B cells in SS. METHODS: Various concentrations of butyrate were intraperitoneally injected three times per week in NOD/ShiLtJ (NOD) mice, the prototype animal model for SS, and observed for more than 10 weeks. Whole salivary flow rate and the histopathology of salivary glands were investigated. Human submandibular gland (HSG) cells and B cells in mouse spleen were used to confirm the anti-inflammatory and immunomodulatory effects of butyrate. RESULTS: Butyrate increased salivary flow rate in NOD mice and reduced inflammation of salivary gland tissues. It also regulated cell death and the expression of circadian-clock-related genes in HSG cells. Butyrate induced B cell regulation by increasing IL-10-producing B (B10) cells and decreasing IL-17-producing B cells, through the circadian clock genes RAR-related orphan receptor alpha and nuclear receptor subfamily 1 group D member 1. CONCLUSION: The findings of this study imply that butyrate may ameliorate SS via reciprocal regulation of IL-10- and IL-17-producing B cells.

Frequency Domain Deep Learning With Non-Invasive Features for Intraoperative Hypotension Prediction.

BACKGROUND: Intraoperative hypotension can lead to postoperative organ dysfunction. Previous studies primarily used invasive arterial pressure as the key biosignal for the detection of hypotension. However, these studies had limitations in incorporating different biosignal modalities and utilizing the periodic nature of biosignals. To address these limitations, we utilized frequency-domain information, which provides key insights that time-domain analysis cannot provide, as revealed by recent advances in deep learning. With the frequency-domain information, we propose a deep-learning approach that integrates multiple biosignal modalities. METHODS: We used the discrete Fourier transform technique, to extract frequency information from biosignal data, which we then combined with the original time-domain data as input for our deep learning model. To improve the interpretability of our results, we incorporated recent interpretable modules for deep-learning models into our analysis. RESULTS: We constructed 75,994 segments from the data of 3,226 patients to predict hypotension during surgery. Our proposed frequency-domain deep-learning model outperformed conventional approaches that rely solely on time-domain information. Notably, our model achieved a greater increase in AUROC performance than the time-domain deep learning models when trained on non-invasive biosignal data only (AUROC 0.898 [95% CI: 0.885-0.91] vs. 0.853 [95% CI: 0.839-0.867]). Further analysis revealed that the 1.5-3.0 Hz frequency band played an important role in predicting hypotension events. CONCLUSION: Utilizing the frequency domain not only demonstrated high performance on invasive data but also showed significant performance improvement when applied to non-invasive data alone. Our proposed framework offers clinicians a novel perspective for predicting intraoperative hypotension.

Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies.

BACKGROUND: Integrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of multiple genomic profiles, several studies have suggested utilizing pathway information rather than using individual genomic profiles. METHODS: We have recently proposed an integrative directed random walk-based method utilizing pathway information (iDRW) for more robust and effective genomic feature extraction. In this study, we applied iDRW to multiple genomic profiles for two different cancers, and designed a directed gene-gene graph which reflects the interaction between gene expression and copy number data. In the experiments, the performances of the iDRW method and four state-of-the-art pathway-based methods were compared using a survival prediction model which classifies samples into two survival groups. RESULTS: The results show that the integrative analysis guided by pathway information not only improves prediction performance, but also provides better biological insights into the top pathways and genes prioritized by the model in both the neuroblastoma and the breast cancer datasets. The pathways and genes selected by the iDRW method were shown to be related to the corresponding cancers. CONCLUSIONS: In this study, we demonstrated the effectiveness of a directed random walk-based multi-omics data integration method applied to gene expression and copy number data for both breast cancer and neuroblastoma datasets. We revamped a directed gene-gene graph considering the impact of copy number variation on gene expression and redefined the weight initialization and gene-scoring method. The benchmark result for iDRW with four pathway-based methods demonstrated that the iDRW method improved survival prediction performance and jointly identified cancer-related pathways and genes for two different cancer datasets. REVIEWERS: This article was reviewed by Helena Molina-Abril and Marta Hidalgo.